d2ski
Tue Feb 17 21:09:08 2015
Based on logistic regression discussed in “Introduction to Statistical Learning” by G.James, D.Witten, T.Hastie, R.Tibshirani
App link: https://d2ski.shinyapps.io/shapp/
The app calculates default probability of a person given his/her:
The simple prediction algorithm is logistic regression model based on simulated dataset provided in ISLR R-package.
The source of the data is ISLR R-package from “Introduction to Statistical Learning” by G.James, D.Witten, T.Hastie, R.Tibshirani.
require(ISLR)
data(Default)
str(Default)
'data.frame': 10000 obs. of 4 variables:
$ default: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ student: Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 2 1 1 ...
$ balance: num 730 817 1074 529 786 ...
$ income : num 44362 12106 31767 35704 38463 ...
Logistic regression model was used.
glm.fit <- glm(default~balance+income+student, data=Default, family=binomial)
glm.fit$formula
default ~ balance + income + student
glm.fit$coef
(Intercept) balance income studentYes
-1.087e+01 5.737e-03 3.033e-06 -6.468e-01
Probability is calculated using the formula:
\( \hat{p}(X)=\frac{e^{\hat{\beta_0}X_0 + \hat{\beta_1}X_1 + \hat{\beta_2}X_2 + \hat{\beta_3}X_3}}{1+e^{\hat{\beta_0}X_0 + \hat{\beta_1}X_1 + \hat{\beta_2}X_2 + \hat{\beta_3}X_3}} \)
For example, for a student with credit card balance 1,500 USD and income 40,000 USD probability of default \( \hat{p}(X)=0.058 \).
Please, note that the data was simulated, so the algorithm is not related to real life and was developed and can be used for education purposes only.
For more information, please, check chapter 4.3 of “Introduction to Statistical Learning” by G.James, D.Witten, T.Hastie, R.Tibshirani.
App link: https://d2ski.shinyapps.io/shapp/